This dataset is a public large-scale HD map dataset for autonomous driving machines developed by NAVER LABS
NAVER LABS has developed a full stack of autonomous technologies for
urban autonomous driving. Among them, our own HD mapping and localization technologies are an indispensable component for ensuring
safe
autonomy. We have defined and built high definition (HD) maps of several complex urban areas in Korea
and verified the practical usability of our HD maps for localization.
NAVER LABS HD Map dataset is a public large-scale HD map dataset for
autonomous driving machines developed by NAVER LABS. We open this dataset to the public in the hope
that the development of autonomous driving technology will be accelerated through public research.
NAVER LABS R1 is a mobile mapping system (MMS) designed to create a high definition (HD) map for self-driving vehicles. As a comparatively lightweight MMS, R1 features a variety of sensors including multiple cameras, 2D & 3D LiDAR's, GPS, IMU, FOG and wheel encoders. Our map construction process integrates the sensor data obtained from R1 with 3D road layout information extracted from aerial photographs to create a hybrid HD map.
NAVER LABS mapping technology is based on the integration of city-scale aerial photographs with data from a mobile mapping system. We extract information about the layout of the road surface (3D Road Layout) from aerial images. Then we integrate it with 3D point cloud collected by R1, our lightweight mobile mapping system (MMS). Compared to conventional HD maps constructed by MMS vehicles, our mapping process can significantly reduce the production costs and time. We also provide a separate localization dataset containing sensor data and pseudo ground truth poses which can be used for evaluation purposes.
More InformationOur HD map dataset consists of three components: 1) 3D Road Layout - the types and locations of visual structures on the road surface such as lanes, road markings, crosswalks and speed bumps; 2) LiDAR Feature Data - 3D LiDAR point cloud of the static road environment captured from our MMS vehicle and semantic labels for each point; and 3) Visual Feature Data - visual features that are compact, discriminative and invariant across different viewing conditions, allowing for reliable matching and localization.
It is important to accurately estimate the current location of the vehicle in autonomous driving situations. The localization technology of NAVER LABS utilizes diverse sensors such as LiDAR, Cameras, GPS, IMU and Wheel Encoders. The localization dataset provides a collection of raw data from these sensors and corresponding pseudo ground truth poses for the purpose of evaluating your own localization algorithms.
Please check out our HD Map & Localization (MMS) dataset.
Pangyo and
Sangam datasets are now available!
Please check the following information before using the dataset.
- UPDATE (2019.10.28) Dataset Released!
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